Abstract: Motion planning has progressed over the last couple of decades in
addressing complex challenges in robotics. An important milestone
was the development of practical sampling-based solutions, for
which recently the conditions that allow these methods to achieve
asymptotic optimality have been identified. Based on the
state-of-the-art, this talk will highlight a series of recent
foundational contributions by our research group in this area:
a) probabilistic near-optimality bounds for sampling-based
planners after a finite amount of computation;
b) small, sparse roadmaps that can quickly return near-optimal
paths based on results from graph theory;
c) optimality guarantees for kinodynamic planning, even when a
steering function is not available.
To solve more complex robotics problems, motion planners need to
be integrated with higher-level reasoning. This talk will present
a framework for the efficient rearrangement of multiple similar
objects using a Baxter robot arm. The framework integrates
sampling-based algorithms for manipulation with combinatorial
solvers for pebble motion graph problems. The talk will conclude
on how such algorithmic, foundational progress together with
technological developments, like cloud computing, new compliant
arms and capable hands, bring the hope of credible co-robots in
terms of i) Collaboration skills when interacting with people or
other robots; ii) Resourcefulness when operating in unstructured
human spaces and iii) Dexterity in interacting with the
environment.
- This event has passed.